期刊文献+

基于预测式错误恢复机制的多描述视频编码研究

Multiple Description Video Coding Research Based on Predictive Error Resilience
下载PDF
导出
摘要 该文提出一种基于预测式错误恢复机制的多描述视频编码方法。在编码端通过预测单路解码可能产生的错误影响,为每个描述分配必要的冗余信息。考虑到视频编码压缩的效率问题,设计了不同的编码模式来处理冗余信息。解码端可以充分利用冗余信息,从而实现丢失视频帧的高质量恢复。实验表明,与传统时域采样方法相比,该方法具有更好的率失真性能。 A novel Multiple Description video coding scheme is proposed based on Predictive Error Resilience (MD-PER). At the encoder, the possible error caused by the single-channel reconstruction can be predicted firstly, and then the necessary redundancy information is inserted for each description. In view of the compression efficiency, different coding modes are designed to compress the generated redundant information. At the decoder, the redundancy information can be applied to achieve high quality recovery of the lost video frames. Experimental results demonstrate that compared with the traditional temporal sampling method the proposed scheme achieves better rate-distortion performance.
出处 《电子与信息学报》 EI CSCD 北大核心 2014年第4期817-822,共6页 Journal of Electronics & Information Technology
基金 国家自然科学基金(61272051) 教育部创新团队发展计划(IRT201206)资助课题
关键词 多描述视频编码 标准视频编码器 预测式错误恢复机制 Multiple description video coding Standard video encoder Predictive error resilience
  • 相关文献

参考文献5

二级参考文献61

  • 1程国华,周源华,赵谊虹.一种对信道差错鲁棒的多描述编码算法[J].上海交通大学学报,2004,38(11):1781-1784. 被引量:1
  • 2Goyal V K. Multiple description coding: compression meets the network [J]. IEEE Signal Processing Magazine, 2001,18(5): 74-93.
  • 3Donoho D. Compressed sensing [J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
  • 4Candes E and Wakin M. An introduction to compressive sampling: a sensing/sampling paradigm that goes against the common knowledge in data acquisition [J]. IEEE Signal Processing Magazine, 2008, 25(2): 21-30.
  • 5Baraniuk R. Compressive sensing [J]. IEEE Signal Processing Magazine, 2007, 24(4): 118-121.
  • 6Donoho D. For most large underdetermined systems of linear equations, the minimal ell-1 norm near-solution approximates the sparsest near-solution. Communications on Pure and Applied Mathematics, 2006, 59(7): 907-934.
  • 7Candes E and Romberg J. Quantitative robust uncertainty principles and optimally sparse decompositions [J]. Foundation of Computational Mathematics, 2006, 6(2): 227-254.
  • 8Gan L. Block compressed sensing of natural images [C]. The 15th International Conference on Digital Signal Processing, Cardiff, UK, 2007: 403-406.
  • 9Duarte M, Davenport M, and Takhar D, et al.. Single-pixel imaging via compressive sampling [J]. IEEE Signal Processing Magazine, 2008, 25(2): 83-91.
  • 10Donoho D, Tsaig Y, and Drori I, et al.. Sparse solution of underdetermined linear equations by stage wise orthogonal matching pursuit [R]. Tech. Report. 2006, Stanford, Department of Statistics, 2006.

共引文献22

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部